Scatter plot
library(moderndive)
library(ggplot2)
### Color Legend ----
# blue line is no interaction -> parallel effect
# red line is with interaction -> not parallel
# black dot line is overall effect
# Daily Plot on food waste -----------------------------------------------
daily_waste <- df %>%
filter(is_closed %in% FALSE) %>%
ggplot(., aes(x = as.Date(date), y = food_waste_kg)) +
geom_point() +
stat_smooth(aes(color = 'Overall'), method = "lm", formula = y ~ x,
linetype = "dashed", se = FALSE) +
geom_parallel_slopes(aes(group = container, color = 'No interaction'),
se = FALSE) +
stat_smooth(aes(group = container, color = 'interaction'),
method = "lm", formula = y ~ x, se = FALSE) +
scale_x_date(date_labels = "%b %d") +
scale_color_manual(name="Model Type",
breaks = c('Overall','No interaction','interaction'),
values = c('Overall' ='black',
'No interaction'='blue',
'interaction'='red')) +
theme(legend.position = "right") +
xlab("Date") + ylab("Daily Food Waste (kg)") +
ggtitle("Container Charge Effect Food Waste")
daily_waste
# Daily Plot on solid food waste -----------------------------------------
daily_solid_waste <-
df %>% filter(is_closed %in% FALSE) %>%
ggplot(data = ., aes(x = as.Date(date), y = solid_waste_kg)) +
geom_point() +
stat_smooth(aes(color = 'Overall'), method = "lm", formula = y ~ x,
linetype = "dashed", se = FALSE) +
geom_parallel_slopes(aes(group = container, color = 'No interaction'),
se = FALSE) +
stat_smooth(aes(group = container, color = 'interaction'),
method = "lm", formula = y ~ x, se = FALSE) +
scale_x_date(date_labels = "%b %d") +
scale_color_manual(name="Model Type",
breaks = c('Overall','No interaction','interaction'),
values = c('Overall' ='black',
'No interaction'='blue',
'interaction'='red')) +
theme(legend.position = "right") +
xlab("Date") + ylab("Daily Solid Food Waste (kg)") +
ggtitle("Container Charge Effect on Solid Food Waste")
daily_solid_waste
# Daily Plot on liquid food waste ----------------------------------------
daily_liquid_waste <-
df %>% filter(is_closed %in% FALSE) %>%
ggplot(data = ., aes(x = as.Date(date), y = liquid_waste_kg)) +
geom_point() +
stat_smooth(aes(color = 'Overall'), method = "lm",
formula = y ~ x, linetype = "dashed", se = FALSE) +
geom_parallel_slopes(aes(group = container, color = 'No interaction'),
se = FALSE) +
stat_smooth(aes(group = container, color = 'interaction'),
method = "lm", formula = y ~ x, se = FALSE) +
scale_x_date(date_labels = "%b %d") +
scale_color_manual(name="Model Type",
breaks = c('Overall','No interaction','interaction'),
values = c('Overall' ='black',
'No interaction'='blue',
'interaction'='red')) +
theme(legend.position = "right") +
xlab("Date") + ylab("Daily Liquid ood Waste (kg)") +
ggtitle("Container Charge Effect on Liquid Food Waste")
daily_liquid_waste
# grid.arrange(daily_waste,
# daily_solid_waste,daily_liquid_waste)
Scatter plot per Customer
library(moderndive)
# Daily Plot on food waste -----------------------------------------------
daily_waste_p <-
df %>% filter(is_closed %in% FALSE) %>%
ggplot(data = ., aes(x = as.Date(date), y = food_waste_p_kg)) +
geom_point() +
stat_smooth(aes(color = 'Overall'), method = "lm", formula = y ~ x,
linetype = "dashed", se = FALSE) +
geom_parallel_slopes(aes(group = container, color = 'No interaction'),
se = FALSE) +
stat_smooth(aes(group = container, color = 'interaction'),
method = "lm", formula = y ~ x, se = FALSE) +
stat_smooth(aes(group = container, color = 'Poly'),
method = "lm", formula = y ~ poly(x,2), se = FALSE) +
scale_color_manual(name="Model Type",
breaks = c('Overall','No interaction','interaction','Poly'),
values = c('Overall' ='black',
'No interaction'='blue',
'interaction'='red',
'Poly' = 'dark green')) +
theme(legend.position = "right") +
scale_x_date(date_labels = "%b %d") +
xlab("Date") + ylab("Food Waste per Customer (kg)") +
ggtitle("Container Charge Effect on Food Waste per Customer")
daily_waste_p
# Daily Plot on solid food waste -----------------------------------------
daily_solid_waste_p <-
df %>% filter(is_closed %in% FALSE) %>%
ggplot(data = ., aes(x = as.Date(date), y = solid_waste_p_kg)) +
geom_point() +
stat_smooth(aes(color = 'Overall'), method = "lm", formula = y ~ x,
linetype = "dashed", se = FALSE) +
geom_parallel_slopes(aes(group = container, color = 'No interaction'),
se = FALSE) +
stat_smooth(aes(group = container, color = 'interaction'), method = "lm",
formula = y ~ x, se = FALSE) +
stat_smooth(aes(group = container, color = 'Poly'),
method = "lm", formula = y ~ poly(x,2), se = FALSE) +
scale_color_manual(name="Model Type",
breaks = c('Overall','No interaction','interaction','Poly'),
values = c('Overall' ='black',
'No interaction'='blue',
'interaction'='red',
'Poly' = 'dark green')) +
theme(legend.position = "right") +
scale_x_date(date_labels = "%b %d") +
xlab("Date") + ylab("Solid Food Waste per Customer (kg)") +
ggtitle("Container Charge Effect on Solid Food Waste per Customer")
daily_solid_waste_p
# Daily Plot on liquid food waste ----------------------------------------
daily_liquid_waste_p <-
df %>% filter(is_closed %in% FALSE) %>%
ggplot(data = ., aes(x = as.Date(date), y = liquid_waste_p_kg)) +
geom_point() +
stat_smooth(aes(color = 'Overall'), method = "lm", formula = y ~ x,
linetype = "dashed", se = FALSE) +
geom_parallel_slopes(aes(group = container, color = 'No interaction'),
se = FALSE) +
stat_smooth(aes(group = container, color = 'interaction'),
method = "lm", formula = y ~ x, se = FALSE) +
stat_smooth(aes(group = container, color = 'Poly'),
method = "lm", formula = y ~ poly(x,2), se = FALSE) +
scale_color_manual(name="Model Type",
breaks = c('Overall','No interaction','interaction','Poly'),
values = c('Overall' ='black',
'No interaction'='blue',
'interaction'='red',
'Poly' = 'dark green')) +
theme(legend.position = "right") +
scale_x_date(date_labels = "%b %d") +
xlab("Date") + ylab("Liquid Food Waste per Customer (kg)") +
ggtitle("Container Charge Effect on Liquid Food Waste per Customer")
daily_liquid_waste_p
# grid.arrange(daily_loss_waste,daily_loss, daily_waste,
# daily_solid_waste,daily_liquid_waste)
RDinT Analysis
library(dplyr)
df <- df %>%
filter(is_closed %in% FALSE) %>%
mutate(time = seq(1:sum(!df$is_closed)))
cutoff <- df %>% filter(date %in% as.Date('2023-01-03')) %>% dplyr::select(time) %>% as.numeric()
df <- df %>% mutate(time = time - cutoff)
Interaction
# Multi-output model
## Target outcomes:
# 1. food_waste_p_kg
# 2. solid_waste_p_kg
# 3. liquid_waste_p_kg
## predictors: temp_c, humi_p, prcp_mm,
# tueE, wedE, thuE, friE, satE,
# container, liquors, sales, halfs
rdts_int_fw <- df %>%
filter(!is_closed) %>%
mutate(var. = cbind(container, time, container*time)) %>%
mutate(outputs = cbind(food_waste_kg,
solid_waste_kg, liquid_waste_kg)) %>%
lm(outputs ~ var., data =.)
summary(rdts_int_fw)
## Response food_waste_kg :
##
## Call:
## lm(formula = food_waste_kg ~ var., data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3728 -1.0750 -0.1146 0.7660 3.9877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.640999 0.303450 5.408 2.34e-07 ***
## var.container 0.451440 0.440216 1.025 0.3067
## var.time -0.013091 0.006059 -2.161 0.0322 *
## var. 0.014899 0.009594 1.553 0.1225
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 157 degrees of freedom
## Multiple R-squared: 0.02956, Adjusted R-squared: 0.01101
## F-statistic: 1.594 on 3 and 157 DF, p-value: 0.1931
##
##
## Response solid_waste_kg :
##
## Call:
## lm(formula = solid_waste_kg ~ var., data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7752 -0.3082 -0.1020 0.2648 2.1840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.627004 0.107778 5.818 3.25e-08 ***
## var.container 0.002645 0.156354 0.017 0.987
## var.time -0.003309 0.002152 -1.538 0.126
## var. 0.003462 0.003408 1.016 0.311
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4954 on 157 degrees of freedom
## Multiple R-squared: 0.03307, Adjusted R-squared: 0.01459
## F-statistic: 1.79 on 3 and 157 DF, p-value: 0.1514
##
##
## Response liquid_waste_kg :
##
## Call:
## lm(formula = liquid_waste_kg ~ var., data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.59768 -0.82269 -0.00855 0.56427 2.82840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.013995 0.214848 4.720 5.19e-06 ***
## var.container 0.448795 0.311680 1.440 0.1519
## var.time -0.009783 0.004290 -2.280 0.0239 *
## var. 0.011437 0.006793 1.684 0.0942 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9875 on 157 degrees of freedom
## Multiple R-squared: 0.03439, Adjusted R-squared: 0.01594
## F-statistic: 1.864 on 3 and 157 DF, p-value: 0.1379
# simple food waste -----
# Formula:
rdt_int_fw <- food_waste_kg ~ container * time
rdt_int_fw <- df %>%
filter(!is_closed) %>%
lm(rdt_int_fw, data = .)
summary(rdt_int_fw)
##
## Call:
## lm(formula = rdt_int_fw, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.3728 -1.0750 -0.1146 0.7660 3.9877
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.640999 0.303450 5.408 2.34e-07 ***
## container 0.451440 0.440216 1.025 0.3067
## time -0.013091 0.006059 -2.161 0.0322 *
## container:time 0.014899 0.009594 1.553 0.1225
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.395 on 157 degrees of freedom
## Multiple R-squared: 0.02956, Adjusted R-squared: 0.01101
## F-statistic: 1.594 on 3 and 157 DF, p-value: 0.1931
#####
# summary(rdt_fw <- lm(formula = rdt_fw,
# data = df, subset = (!df$is_closed)))
# simple solid food waste -----
rdt_int_sfw <- solid_waste_kg ~ container * time
rdt_int_sfw <- df %>%
filter(!is_closed) %>%
lm(rdt_int_sfw, data = .)
summary(rdt_int_sfw)
##
## Call:
## lm(formula = rdt_int_sfw, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.7752 -0.3082 -0.1020 0.2648 2.1840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.627004 0.107778 5.818 3.25e-08 ***
## container 0.002645 0.156354 0.017 0.987
## time -0.003309 0.002152 -1.538 0.126
## container:time 0.003462 0.003408 1.016 0.311
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4954 on 157 degrees of freedom
## Multiple R-squared: 0.03307, Adjusted R-squared: 0.01459
## F-statistic: 1.79 on 3 and 157 DF, p-value: 0.1514
# simple liquid food waste -----
rdt_int_lfw <- liquid_waste_kg ~ container * time
rdt_int_lfw <- df %>%
filter(!is_closed) %>%
lm(rdt_int_lfw, data = .)
summary(rdt_int_lfw)
##
## Call:
## lm(formula = rdt_int_lfw, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.59768 -0.82269 -0.00855 0.56427 2.82840
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.013995 0.214848 4.720 5.19e-06 ***
## container 0.448795 0.311680 1.440 0.1519
## time -0.009783 0.004290 -2.280 0.0239 *
## container:time 0.011437 0.006793 1.684 0.0942 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.9875 on 157 degrees of freedom
## Multiple R-squared: 0.03439, Adjusted R-squared: 0.01594
## F-statistic: 1.864 on 3 and 157 DF, p-value: 0.1379
Ass-Interaction
- Linearity of the relationships between the dependent and independent
variables
- Normality of the residuals
- Homoscedasticity of the residuals
- No influential points (outliers)
- No multicollinearity
- Independence of the observations
library(performance)
ass_int_fw <- plot(check_model(rdt_int_fw, detrend=FALSE, panel = FALSE))
ass_int_sfw <- plot(check_model(rdt_int_sfw, detrend=FALSE, panel = FALSE))
ass_int_lfw <- plot(check_model(rdt_int_lfw, detrend=FALSE, panel = FALSE))
# 1. Linearity of the relationships between the dependent and independent variables
# 1.1 plot residual vs fitted values
ass_int_fw[[2]] + labs(title = "Linearity: Food Waste", subtitle = "")
ass_int_sfw[[2]] + labs(title = "Linearity: Solid Food Waste", subtitle = "")
ass_int_lfw[[2]] + labs(title = "Linearity: Liquid Food Waste", subtitle = "")
# 1.2 check linearity
check_heteroscedasticity(rdt_int_fw)
## Warning: Heteroscedasticity (non-constant error variance) detected (p = 0.006).
check_heteroscedasticity(rdt_int_sfw)
## Warning: Heteroscedasticity (non-constant error variance) detected (p < .001).
check_heteroscedasticity(rdt_int_lfw)
## Warning: Heteroscedasticity (non-constant error variance) detected (p = 0.023).
# 2. Normality of the residuals
# 2.1 histogram of residuals
# Normality of Residuals: Food Waste
plot(check_normality(rdt_int_fw), type = "density") +
labs(title = "Normality of Residuals: Food Waste", subtitle = "")
# Normality of Residuals: Solid Food Waste
plot(check_normality(rdt_int_sfw), type = "density") +
labs(title = "Normality of Residuals: Solid Food Waste", subtitle = "")
# Normality of Residuals: Liquid Food Waste
plot(check_normality(rdt_int_lfw), type = "density") +
labs(title = "Normality of Residuals: Liquid Food Waste", subtitle = "")
# 2.2 Normality of Residuals
ass_int_fw[[6]] + labs(title = "QQ Plot of Residuals: Food Waste", subtitle = "")
ass_int_sfw[[6]] + labs(title = "QQ Plot of Residuals: Solid Food Waste", subtitle = "")
ass_int_lfw[[6]] + labs(title = "QQ Plot of Residuals: Liquid Food Waste", subtitle = "")
# 2.3 shapiro-wilk normality test
check_normality(rdt_int_fw)
## Warning: Non-normality of residuals detected (p < .001).
check_normality(rdt_int_sfw)
## Warning: Non-normality of residuals detected (p < .001).
check_normality(rdt_int_lfw)
## Warning: Non-normality of residuals detected (p < .001).
# 3. Homoscedasticity of the residuals
# 3.1 plot residuals
ass_int_fw[[3]] + labs(title = "Homoscedasticity: Food Waste", subtitle = "")
ass_int_sfw[[3]] + labs(title = "Homoscedasticity: Solid Food Waste", subtitle = "")
ass_int_lfw[[3]] + labs(title = "Homoscedasticity: Liquid Food Waste", subtitle = "")
# 3.2 Breusch-Pagan test
lmtest::bptest(rdt_int_fw)
##
## studentized Breusch-Pagan test
##
## data: rdt_int_fw
## BP = 7.8124, df = 3, p-value = 0.05005
lmtest::bptest(rdt_int_sfw)
##
## studentized Breusch-Pagan test
##
## data: rdt_int_sfw
## BP = 5.4746, df = 3, p-value = 0.1402
lmtest::bptest(rdt_int_lfw)
##
## studentized Breusch-Pagan test
##
## data: rdt_int_lfw
## BP = 7.3214, df = 3, p-value = 0.06233
# 4. No influential points (outliers)
ass_int_fw[[4]] + labs(title = "Outliers: Food Waste", subtitle = "")
ass_int_sfw[[4]] + labs(title = "Outliers: Solid Food Waste", subtitle = "")
ass_int_lfw[[4]] + labs(title = "Outliers: Liquid Food Waste", subtitle = "")
# 5. No multicollinearity
ass_int_fw[[5]] + labs(title = "VIF: Food Waste", subtitle = "")
ass_int_sfw[[5]] + labs(title = "VIF: Solid Food Waste", subtitle = "")
ass_int_lfw[[5]] + labs(title = "VIF: Liquid Food Waste", subtitle = "")
# 6. Independence of the observations
# Autocorrelation
check_autocorrelation(rdt_int_fw)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.478).
check_autocorrelation(rdt_int_sfw)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.474).
check_autocorrelation(rdt_int_lfw)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.472).
Multiple model
# multi food waste -----
rdt_multi_fw <- food_waste_kg ~ container * time +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_multi_fw <- df %>%
filter(!is_closed) %>%
lm(rdt_multi_fw, data = .)
summary(rdt_multi_fw)
##
## Call:
## lm(formula = rdt_multi_fw, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0485 -0.6500 -0.0912 0.4476 3.3391
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.2312704 0.9452769 -2.360 0.0195 *
## container 0.5782882 0.3674976 1.574 0.1177
## time -0.0083069 0.0065926 -1.260 0.2096
## temp_c -0.0105319 0.0126725 -0.831 0.4072
## humi_p 0.0091364 0.0093094 0.981 0.3280
## prcp_mm -0.0393178 0.0405061 -0.971 0.3333
## liquors -0.0103638 0.0509853 -0.203 0.8392
## sales 0.0040949 0.0005081 8.059 2.17e-13 ***
## halfs 0.0715002 0.0294293 2.430 0.0163 *
## container:time 0.0093167 0.0100585 0.926 0.3558
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.008 on 151 degrees of freedom
## Multiple R-squared: 0.5122, Adjusted R-squared: 0.4832
## F-statistic: 17.62 on 9 and 151 DF, p-value: < 2.2e-16
# multi solid food waste -----
rdt_multi_sfw <- solid_waste_kg ~ container * time +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_multi_sfw <- df %>%
filter(!is_closed) %>%
lm(rdt_multi_sfw, data = .)
summary(rdt_multi_sfw)
##
## Call:
## lm(formula = rdt_multi_sfw, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.67954 -0.25736 -0.07937 0.18700 2.32548
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.4376693 0.3876971 -1.129 0.261
## container 0.1019002 0.1507259 0.676 0.500
## time -0.0025510 0.0027039 -0.943 0.347
## temp_c -0.0056240 0.0051975 -1.082 0.281
## humi_p 0.0014782 0.0038182 0.387 0.699
## prcp_mm -0.0154605 0.0166132 -0.931 0.354
## liquors 0.0080184 0.0209112 0.383 0.702
## sales 0.0012142 0.0002084 5.826 3.31e-08 ***
## halfs 0.0130254 0.0120702 1.079 0.282
## container:time 0.0016192 0.0041254 0.392 0.695
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4135 on 151 degrees of freedom
## Multiple R-squared: 0.3519, Adjusted R-squared: 0.3133
## F-statistic: 9.112 on 9 and 151 DF, p-value: 5.999e-11
# multi liquid food waste -----
rdt_multi_lfw <- liquid_waste_kg ~ container * time +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_multi_lfw <- df %>%
filter(!is_closed) %>%
lm(rdt_multi_lfw, data = .)
summary(rdt_multi_lfw)
##
## Call:
## lm(formula = rdt_multi_lfw, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.55792 -0.44014 -0.07213 0.38966 1.87255
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.7936011 0.6627069 -2.706 0.00758 **
## container 0.4763880 0.2576422 1.849 0.06641 .
## time -0.0057559 0.0046219 -1.245 0.21493
## temp_c -0.0049079 0.0088843 -0.552 0.58148
## humi_p 0.0076581 0.0065265 1.173 0.24249
## prcp_mm -0.0238573 0.0283977 -0.840 0.40217
## liquors -0.0183822 0.0357444 -0.514 0.60782
## sales 0.0028807 0.0003562 8.086 1.85e-13 ***
## halfs 0.0584748 0.0206321 2.834 0.00522 **
## container:time 0.0076975 0.0070517 1.092 0.27676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7069 on 151 degrees of freedom
## Multiple R-squared: 0.5242, Adjusted R-squared: 0.4958
## F-statistic: 18.48 on 9 and 151 DF, p-value: < 2.2e-16
Ass-Multiple
- Linearity of the relationships between the dependent and independent
variables
- Normality of the residuals
- Homoscedasticity of the residuals
- No influential points (outliers)
- No multicollinearity
- Independence of the observations
library(performance)
ass_multi_fw <- plot(check_model(rdt_multi_fw, detrend=FALSE,panel = FALSE))
ass_multi_sfw <- plot(check_model(rdt_multi_sfw, detrend=FALSE,panel = FALSE))
ass_multi_lfw <- plot(check_model(rdt_multi_lfw, detrend=FALSE,panel = FALSE))
# 1. Linearity of the relationships between the dependent and independent variables
# 1.1 plot residual vs fitted values
ass_multi_fw[[2]] + labs(title = "Linearity: Food Waste", subtitle = "")
ass_multi_sfw[[2]] + labs(title = "Linearity: Solid Food Waste", subtitle = "")
ass_multi_lfw[[2]] + labs(title = "Linearity: Liquid Food Waste", subtitle = "")
# 1.2 check linearity
check_heteroscedasticity(rdt_multi_fw)
## Warning: Heteroscedasticity (non-constant error variance) detected (p = 0.008).
check_heteroscedasticity(rdt_multi_sfw)
## Warning: Heteroscedasticity (non-constant error variance) detected (p = 0.006).
check_heteroscedasticity(rdt_multi_lfw)
## Warning: Heteroscedasticity (non-constant error variance) detected (p = 0.003).
# 2. Normality of the residuals
# 2.1 histogram of residuals
# Normality of Residuals: Food Waste
plot(check_normality(rdt_multi_fw), type = "density") +
labs(title = "Normality of Residuals: Food Waste", subtitle = "")
# Normality of Residuals: Solid Food Waste
plot(check_normality(rdt_multi_sfw), type = "density") +
labs(title = "Normality of Residuals: Solid Food Waste", subtitle = "")
# Normality of Residuals: Liquid Food Waste
plot(check_normality(rdt_multi_lfw), type = "density") +
labs(title = "Normality of Residuals: Liquid Food Waste", subtitle = "")
# 2.2 Normality of Residuals
ass_multi_fw[[6]] +
labs(title = "QQ Plot of Residuals: Food Waste", subtitle = "")
ass_multi_sfw[[6]] +
labs(title = "QQ Plot of Residuals: Solid Food Waste", subtitle = "")
ass_multi_lfw[[6]] +
labs(title = "QQ Plot of Residuals: Liquid Food Waste", subtitle = "")
# 2.3 shapiro-wilk normality test
check_normality(rdt_multi_fw)
## Warning: Non-normality of residuals detected (p = 0.001).
check_normality(rdt_multi_sfw)
## Warning: Non-normality of residuals detected (p < .001).
check_normality(rdt_multi_lfw)
## Warning: Non-normality of residuals detected (p = 0.023).
# 3. Homoscedasticity of the residuals
# 3.1 plot residuals
ass_multi_fw[[3]] +
labs(title = "Homoscedasticity: Food Waste", subtitle = "")
ass_multi_sfw[[3]] +
labs(title = "Homoscedasticity: Solid Food Waste", subtitle = "")
ass_multi_lfw[[3]] +
labs(title = "Homoscedasticity: Liquid Food Waste", subtitle = "")
# 3.2 Breusch-Pagan test
lmtest::bptest(rdt_multi_fw)
##
## studentized Breusch-Pagan test
##
## data: rdt_multi_fw
## BP = 14.705, df = 9, p-value = 0.09935
lmtest::bptest(rdt_multi_sfw)
##
## studentized Breusch-Pagan test
##
## data: rdt_multi_sfw
## BP = 10.062, df = 9, p-value = 0.3455
lmtest::bptest(rdt_multi_lfw)
##
## studentized Breusch-Pagan test
##
## data: rdt_multi_lfw
## BP = 15.294, df = 9, p-value = 0.08316
# 4. No influential points (outliers)
ass_multi_fw[[4]] + labs(title = "Outliers: Food Waste", subtitle = "")
ass_multi_sfw[[4]] + labs(title = "Outliers: Solid Food Waste", subtitle = "")
ass_multi_lfw[[4]] + labs(title = "Outliers: Liquid Food Waste", subtitle = "")
# 5. No multicollinearity
ass_multi_fw[[5]] + labs(title = "VIF: Food Waste", subtitle = "")
ass_multi_sfw[[5]] + labs(title = "VIF: Solid Food Waste", subtitle = "")
ass_multi_lfw[[5]] + labs(title = "VIF: Liquid Food Waste", subtitle = "")
# 6. Independence of the observations
# Autocorrelation
check_autocorrelation(rdt_multi_fw)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.496).
check_autocorrelation(rdt_multi_sfw)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.560).
check_autocorrelation(rdt_multi_lfw)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.480).
Polynomial model
# poly- food waste -----
rdt_poly_fw <- food_waste_kg ~ container * time +
container * I(time^2) +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_poly_fw <- df %>%
filter(!is_closed) %>%
lm(rdt_poly_fw, data = .)
summary(rdt_poly_fw)
##
## Call:
## lm(formula = rdt_poly_fw, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.0072 -0.6418 -0.1261 0.4642 3.2952
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2.4671604 1.0125798 -2.437 0.0160 *
## container 0.3838594 0.5208439 0.737 0.4623
## time -0.0189069 0.0186297 -1.015 0.3118
## I(time^2) -0.0001330 0.0002020 -0.658 0.5114
## temp_c -0.0083476 0.0127768 -0.653 0.5145
## humi_p 0.0103180 0.0093920 1.099 0.2737
## prcp_mm -0.0513615 0.0414043 -1.240 0.2167
## liquors -0.0068301 0.0510638 -0.134 0.8938
## sales 0.0041346 0.0005102 8.104 1.78e-13 ***
## halfs 0.0703055 0.0296094 2.374 0.0188 *
## container:time 0.0469042 0.0293172 1.600 0.1117
## container:I(time^2) -0.0002283 0.0003461 -0.660 0.5106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.008 on 149 degrees of freedom
## Multiple R-squared: 0.5186, Adjusted R-squared: 0.4831
## F-statistic: 14.59 on 11 and 149 DF, p-value: < 2.2e-16
# poly- solid food waste -----
rdt_poly_sfw <- solid_waste_kg ~ container * time +
container * I(time^2) +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_poly_sfw <- df %>%
filter(!is_closed) %>%
lm(rdt_poly_sfw, data = .)
summary(rdt_poly_sfw)
##
## Call:
## lm(formula = rdt_poly_sfw, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.69124 -0.24652 -0.06983 0.18138 2.25175
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -7.004e-01 4.129e-01 -1.696 0.0919 .
## container 1.862e-01 2.124e-01 0.877 0.3821
## time -1.498e-02 7.596e-03 -1.971 0.0505 .
## I(time^2) -1.467e-04 8.237e-05 -1.781 0.0770 .
## temp_c -4.899e-03 5.210e-03 -0.940 0.3486
## humi_p 2.446e-03 3.830e-03 0.639 0.5240
## prcp_mm -2.077e-02 1.688e-02 -1.230 0.2206
## liquors 1.013e-02 2.082e-02 0.487 0.6272
## sales 1.249e-03 2.080e-04 6.005 1.4e-08 ***
## halfs 1.095e-02 1.207e-02 0.907 0.3658
## container:time 2.138e-02 1.195e-02 1.788 0.0758 .
## container:I(time^2) 5.136e-05 1.411e-04 0.364 0.7164
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.4112 on 149 degrees of freedom
## Multiple R-squared: 0.3679, Adjusted R-squared: 0.3212
## F-statistic: 7.884 on 11 and 149 DF, p-value: 1.063e-10
# poly- liquid food waste -----
rdt_poly_lfw <- liquid_waste_kg ~ container * time +
container * I(time^2) +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_poly_lfw <- df %>%
filter(!is_closed) %>%
lm(rdt_poly_lfw, data = .)
summary(rdt_poly_lfw)
##
## Call:
## lm(formula = rdt_poly_lfw, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.53737 -0.42153 -0.08896 0.35757 1.87042
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -1.7668065 0.7103879 -2.487 0.01398 *
## container 0.1976864 0.3654045 0.541 0.58931
## time -0.0039315 0.0130699 -0.301 0.76398
## I(time^2) 0.0000137 0.0001417 0.097 0.92311
## temp_c -0.0034488 0.0089637 -0.385 0.70097
## humi_p 0.0078720 0.0065890 1.195 0.23410
## prcp_mm -0.0305946 0.0290477 -1.053 0.29393
## liquors -0.0169643 0.0358244 -0.474 0.63652
## sales 0.0028854 0.0003579 8.061 2.27e-13 ***
## halfs 0.0593525 0.0207728 2.857 0.00489 **
## container:time 0.0255287 0.0205679 1.241 0.21649
## container:I(time^2) -0.0002796 0.0002428 -1.152 0.25130
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.7074 on 149 degrees of freedom
## Multiple R-squared: 0.5297, Adjusted R-squared: 0.495
## F-statistic: 15.26 on 11 and 149 DF, p-value: < 2.2e-16
Ass-Poly
- Linearity of the relationships between the dependent and independent
variables
- Normality of the residuals
- Homoscedasticity of the residuals
- No influential points (outliers)
- No multicollinearity
- Independence of the observations
library(performance)
ass_poly_fw <- plot(check_model(rdt_poly_fw, detrend=FALSE,panel = FALSE))
ass_poly_sfw <- plot(check_model(rdt_poly_sfw, detrend=FALSE,panel = FALSE))
ass_poly_lfw <- plot(check_model(rdt_poly_lfw, detrend=FALSE,panel = FALSE))
# 1. Linearity of the relationships between the dependent and independent variables
# 1.1 plot residual vs fitted values
ass_poly_fw[[2]] + labs(title = "Linearity: Food Waste", subtitle = "")
ass_poly_sfw[[2]] + labs(title = "Linearity: Solid Food Waste", subtitle = "")
ass_poly_lfw[[2]] + labs(title = "Linearity: Liquid Food Waste", subtitle = "")
# 1.2 check linearity
check_heteroscedasticity(rdt_poly_fw)
## Warning: Heteroscedasticity (non-constant error variance) detected (p = 0.013).
check_heteroscedasticity(rdt_poly_sfw)
## Warning: Heteroscedasticity (non-constant error variance) detected (p = 0.003).
check_heteroscedasticity(rdt_poly_lfw)
## Warning: Heteroscedasticity (non-constant error variance) detected (p = 0.007).
# 2. Normality of the residuals
# 2.1 histogram of residuals
# Normality of Residuals: Food Waste
plot(check_normality(rdt_poly_fw), type = "density") +
labs(title = "Normality of Residuals: Food Waste", subtitle = "")
# Normality of Residuals: Solid Food Waste
plot(check_normality(rdt_poly_sfw), type = "density") +
labs(title = "Normality of Residuals: Solid Food Waste", subtitle = "")
# Normality of Residuals: Liquid Food Waste
plot(check_normality(rdt_poly_lfw), type = "density") +
labs(title = "Normality of Residuals: Liquid Food Waste", subtitle = "")
# 2.2 Normality of Residuals
ass_poly_fw[[6]] +
labs(title = "QQ Plot of Residuals: Food Waste", subtitle = "")
ass_poly_sfw[[6]] +
labs(title = "QQ Plot of Residuals: Solid Food Waste", subtitle = "")
ass_poly_lfw[[6]] +
labs(title = "QQ Plot of Residuals: Liquid Food Waste", subtitle = "")
# 2.3 shapiro-wilk normality test
check_normality(rdt_poly_fw)
## Warning: Non-normality of residuals detected (p < .001).
check_normality(rdt_poly_sfw)
## Warning: Non-normality of residuals detected (p < .001).
check_normality(rdt_poly_lfw)
## Warning: Non-normality of residuals detected (p = 0.013).
# 3. Homoscedasticity of the residuals
# 3.1 plot residuals
ass_poly_fw[[3]] +
labs(title = "Homoscedasticity: Food Waste", subtitle = "")
ass_poly_sfw[[3]] +
labs(title = "Homoscedasticity: Solid Food Waste", subtitle = "")
ass_poly_lfw[[3]] +
labs(title = "Homoscedasticity: Liquid Food Waste", subtitle = "")
# 3.2 Breusch-Pagan test
lmtest::bptest(rdt_poly_fw)
##
## studentized Breusch-Pagan test
##
## data: rdt_poly_fw
## BP = 16.435, df = 11, p-value = 0.1257
lmtest::bptest(rdt_poly_sfw)
##
## studentized Breusch-Pagan test
##
## data: rdt_poly_sfw
## BP = 11.26, df = 11, p-value = 0.4218
lmtest::bptest(rdt_poly_lfw)
##
## studentized Breusch-Pagan test
##
## data: rdt_poly_lfw
## BP = 16.008, df = 11, p-value = 0.1408
# 4. No influential points (outliers)
ass_poly_fw[[4]] + labs(title = "Outliers: Food Waste", subtitle = "")
ass_poly_sfw[[4]] + labs(title = "Outliers: Solid Food Waste", subtitle = "")
ass_poly_lfw[[4]] + labs(title = "Outliers: Liquid Food Waste", subtitle = "")
# 5. No multicollinearity
ass_poly_fw[[5]] + labs(title = "VIF: Food Waste", subtitle = "")
ass_poly_sfw[[5]] + labs(title = "VIF: Solid Food Waste", subtitle = "")
ass_poly_lfw[[5]] + labs(title = "VIF: Liquid Food Waste", subtitle = "")
# 6. Independence of the observations
# Autocorrelation
check_autocorrelation(rdt_poly_fw)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.518).
check_autocorrelation(rdt_poly_sfw)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.670).
check_autocorrelation(rdt_poly_lfw)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.474).
Per Customer
Interaction
################ Interaction
# simple food waste per customer -----
rdt_int_fw_p <- food_waste_p_kg ~ container * time
rdt_int_fw_p <- df %>%
filter(!is_closed) %>%
lm(rdt_int_fw_p, data = .)
summary(rdt_int_fw_p)
##
## Call:
## lm(formula = rdt_int_fw_p, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.085830 -0.026913 -0.000256 0.022668 0.144070
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0634025 0.0084860 7.471 5.19e-12 ***
## container 0.0205965 0.0123107 1.673 0.0963 .
## time -0.0002550 0.0001694 -1.505 0.1344
## container:time 0.0002948 0.0002683 1.099 0.2736
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03901 on 157 degrees of freedom
## Multiple R-squared: 0.03369, Adjusted R-squared: 0.01523
## F-statistic: 1.825 on 3 and 157 DF, p-value: 0.1449
# simple solid food waste per customer -----
rdt_int_sfw_p <- solid_waste_p_kg ~ container * time
rdt_int_sfw_p <- df %>%
filter(!is_closed) %>%
lm(rdt_int_sfw_p, data = .)
summary(rdt_int_sfw_p)
##
## Call:
## lm(formula = rdt_int_sfw_p, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.025637 -0.009649 -0.001998 0.007429 0.107324
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.413e-02 3.438e-03 7.018 6.35e-11 ***
## container 6.087e-04 4.988e-03 0.122 0.903
## time -6.278e-05 6.865e-05 -0.914 0.362
## container:time 7.849e-05 1.087e-04 0.722 0.471
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0158 on 157 degrees of freedom
## Multiple R-squared: 0.00791, Adjusted R-squared: -0.01105
## F-statistic: 0.4173 on 3 and 157 DF, p-value: 0.7408
# simple liquid food waste per customer -----
rdt_int_lfw_p <- liquid_waste_p_kg ~ container * time
rdt_int_lfw_p <- df %>%
filter(!is_closed) %>%
lm(rdt_int_lfw_p, data = .)
summary(rdt_int_lfw_p)
##
## Call:
## lm(formula = rdt_int_lfw_p, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.060368 -0.021770 0.000354 0.019225 0.067531
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0392726 0.0060700 6.470 1.19e-09 ***
## container 0.0199878 0.0088058 2.270 0.0246 *
## time -0.0001922 0.0001212 -1.586 0.1148
## container:time 0.0002163 0.0001919 1.127 0.2615
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.0279 on 157 degrees of freedom
## Multiple R-squared: 0.06328, Adjusted R-squared: 0.04538
## F-statistic: 3.535 on 3 and 157 DF, p-value: 0.01621
Ass-Interaction
- Linearity of the relationships between the dependent and independent
variables
- Normality of the residuals
- Homoscedasticity of the residuals
- No influential points (outliers)
- No multicollinearity
- Independence of the observations
library(performance)
ass_int_fw_p <- plot(check_model(rdt_int_fw_p, detrend=FALSE, panel = FALSE))
ass_int_sfw_p <- plot(check_model(rdt_int_sfw_p, detrend=FALSE,panel = FALSE))
ass_int_lfw_p <- plot(check_model(rdt_int_lfw_p, detrend=FALSE,panel = FALSE))
# 1. Linearity of the relationships between the dependent and independent variables
# 1.1 plot residual vs fitted values
ass_int_fw_p[[2]] + labs(title = "Linearity: Food Waste", subtitle = "")
ass_int_sfw_p[[2]] + labs(title = "Linearity: Solid Food Waste", subtitle = "")
ass_int_lfw_p[[2]] + labs(title = "Linearity: Liquid Food Waste", subtitle = "")
# 1.2 check linearity
check_heteroscedasticity(rdt_int_fw_p)
## OK: Error variance appears to be homoscedastic (p = 0.560).
check_heteroscedasticity(rdt_int_sfw_p)
## OK: Error variance appears to be homoscedastic (p = 0.085).
check_heteroscedasticity(rdt_int_lfw_p)
## OK: Error variance appears to be homoscedastic (p = 0.357).
# 2. Normality of the residuals
# 2.1 histogram of residuals
# Normality of Residuals: Food Waste
plot(check_normality(rdt_int_fw_p), type = "density") +
labs(title = "Normality of Residuals: Food Waste", subtitle = "")
# Normality of Residuals: Solid Food Waste
plot(check_normality(rdt_int_sfw_p), type = "density") +
labs(title = "Normality of Residuals: Solid Food Waste", subtitle = "")
# Normality of Residuals: Liquid Food Waste
plot(check_normality(rdt_int_lfw_p), type = "density") +
labs(title = "Normality of Residuals: Liquid Food Waste", subtitle = "")
# 2.2 Normality of Residuals
ass_int_fw_p[[6]] + labs(title = "QQ Plot of Residuals: Food Waste", subtitle = "")
ass_int_sfw_p[[6]] + labs(title = "QQ Plot of Residuals: Solid Food Waste", subtitle = "")
ass_int_lfw_p[[6]] + labs(title = "QQ Plot of Residuals: Liquid Food Waste", subtitle = "")
# 2.3 shapiro-wilk normality test
check_normality(rdt_int_fw_p)
## OK: residuals appear as normally distributed (p = 0.310).
check_normality(rdt_int_sfw_p)
## Warning: Non-normality of residuals detected (p < .001).
check_normality(rdt_int_lfw_p)
## OK: residuals appear as normally distributed (p = 0.506).
# 3. Homoscedasticity of the residuals
# 3.1 plot residuals
ass_int_fw_p[[3]] + labs(title = "Homoscedasticity: Food Waste", subtitle = "")
ass_int_sfw_p[[3]] + labs(title = "Homoscedasticity: Solid Food Waste", subtitle = "")
ass_int_lfw_p[[3]] + labs(title = "Homoscedasticity: Liquid Food Waste", subtitle = "")
# 3.2 Breusch-Pagan test
lmtest::bptest(rdt_int_fw_p)
##
## studentized Breusch-Pagan test
##
## data: rdt_int_fw_p
## BP = 0.39927, df = 3, p-value = 0.9404
lmtest::bptest(rdt_int_sfw_p)
##
## studentized Breusch-Pagan test
##
## data: rdt_int_sfw_p
## BP = 0.78557, df = 3, p-value = 0.8529
lmtest::bptest(rdt_int_lfw_p)
##
## studentized Breusch-Pagan test
##
## data: rdt_int_lfw_p
## BP = 1.5608, df = 3, p-value = 0.6683
# 4. No influential points (outliers)
ass_int_fw_p[[4]] + labs(title = "Outliers: Food Waste", subtitle = "")
ass_int_sfw_p[[4]] + labs(title = "Outliers: Solid Food Waste", subtitle = "")
ass_int_lfw_p[[4]] + labs(title = "Outliers: Liquid Food Waste", subtitle = "")
# 5. No multicollinearity
ass_int_fw_p[[5]] + labs(title = "VIF: Food Waste", subtitle = "")
ass_int_sfw_p[[5]] + labs(title = "VIF: Solid Food Waste", subtitle = "")
ass_int_lfw_p[[5]] + labs(title = "VIF: Liquid Food Waste", subtitle = "")
# 6. Independence of the observations
# Autocorrelation
check_autocorrelation(rdt_int_fw_p)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.660).
check_autocorrelation(rdt_int_sfw_p)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.796).
check_autocorrelation(rdt_int_lfw_p)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.608).
Multiple model
## Multiple model ----
# multi food waste per customer -----
rdt_multi_fw_p <- food_waste_p_kg ~ container * time +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_multi_fw_p <- df %>%
filter(!is_closed) %>%
lm(rdt_multi_fw_p, data = .)
summary(rdt_multi_fw_p)
##
## Call:
## lm(formula = rdt_multi_fw_p, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.074837 -0.024496 -0.002514 0.019536 0.148986
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.272e-02 3.556e-02 0.639 0.5239
## container 2.445e-02 1.382e-02 1.768 0.0790 .
## time -2.128e-04 2.480e-04 -0.858 0.3921
## temp_c -1.980e-04 4.767e-04 -0.415 0.6785
## humi_p 9.816e-05 3.502e-04 0.280 0.7796
## prcp_mm -1.753e-03 1.524e-03 -1.150 0.2519
## liquors 1.114e-03 1.918e-03 0.581 0.5624
## sales 4.082e-05 1.911e-05 2.135 0.0343 *
## halfs 7.092e-04 1.107e-03 0.641 0.5227
## container:time 1.984e-04 3.784e-04 0.524 0.6008
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03793 on 151 degrees of freedom
## Multiple R-squared: 0.1212, Adjusted R-squared: 0.06883
## F-statistic: 2.314 on 9 and 151 DF, p-value: 0.01819
# multi solid food waste per customer -----
rdt_multi_sfw_p <- solid_waste_p_kg ~ container * time +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_multi_sfw_p <- df %>%
filter(!is_closed) %>%
lm(rdt_multi_sfw_p, data = .)
summary(rdt_multi_sfw_p)
##
## Call:
## lm(formula = rdt_multi_sfw_p, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.027494 -0.010028 -0.001538 0.007102 0.104802
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.945e-02 1.477e-02 1.317 0.190
## container 4.123e-03 5.743e-03 0.718 0.474
## time -7.996e-05 1.030e-04 -0.776 0.439
## temp_c -1.554e-04 1.980e-04 -0.785 0.434
## humi_p -3.292e-05 1.455e-04 -0.226 0.821
## prcp_mm -7.440e-04 6.329e-04 -1.175 0.242
## liquors 6.531e-04 7.967e-04 0.820 0.414
## sales 9.221e-06 7.940e-06 1.161 0.247
## halfs -2.701e-04 4.599e-04 -0.587 0.558
## container:time 4.472e-05 1.572e-04 0.285 0.776
##
## Residual standard error: 0.01576 on 151 degrees of freedom
## Multiple R-squared: 0.05157, Adjusted R-squared: -0.00496
## F-statistic: 0.9123 on 9 and 151 DF, p-value: 0.5163
# multi liquid food waste per customer -----
rdt_multi_lfw_p <- liquid_waste_p_kg ~ container * time +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_multi_lfw_p <- df %>%
filter(!is_closed) %>%
lm(rdt_multi_lfw_p, data = .)
summary(rdt_multi_lfw_p)
##
## Call:
## lm(formula = rdt_multi_lfw_p, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.051511 -0.017780 -0.004288 0.016169 0.078517
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.269e-03 2.512e-02 0.130 0.8966
## container 2.032e-02 9.764e-03 2.081 0.0391 *
## time -1.329e-04 1.752e-04 -0.759 0.4493
## temp_c -4.256e-05 3.367e-04 -0.126 0.8996
## humi_p 1.311e-04 2.474e-04 0.530 0.5969
## prcp_mm -1.009e-03 1.076e-03 -0.937 0.3502
## liquors 4.605e-04 1.355e-03 0.340 0.7344
## sales 3.160e-05 1.350e-05 2.340 0.0206 *
## halfs 9.793e-04 7.819e-04 1.252 0.2124
## container:time 1.537e-04 2.673e-04 0.575 0.5661
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02679 on 151 degrees of freedom
## Multiple R-squared: 0.1694, Adjusted R-squared: 0.1198
## F-statistic: 3.421 on 9 and 151 DF, p-value: 0.0007411
Ass-Multiple
- Linearity of the relationships between the dependent and independent
variables
- Normality of the residuals
- Homoscedasticity of the residuals
- No influential points (outliers)
- No multicollinearity
- Independence of the observations
library(performance)
ass_multi_fw_p <- plot(check_model(rdt_multi_fw_p, detrend=FALSE,panel = FALSE))
ass_multi_sfw_p <- plot(check_model(rdt_multi_sfw_p, detrend=FALSE,panel = FALSE))
ass_multi_lfw_p <- plot(check_model(rdt_multi_lfw_p, detrend=FALSE,panel = FALSE))
# 1. Linearity of the relationships between the dependent and independent variables
# 1.1 plot residual vs fitted values
ass_multi_fw_p[[2]] + labs(title = "Linearity: Food Waste", subtitle = "")
ass_multi_sfw_p[[2]] + labs(title = "Linearity: Solid Food Waste", subtitle = "")
ass_multi_lfw_p[[2]] + labs(title = "Linearity: Liquid Food Waste", subtitle = "")
# 1.2 check linearity
check_heteroscedasticity(rdt_multi_fw_p)
## OK: Error variance appears to be homoscedastic (p = 0.077).
check_heteroscedasticity(rdt_multi_sfw_p)
## OK: Error variance appears to be homoscedastic (p = 0.053).
check_heteroscedasticity(rdt_multi_lfw_p)
## OK: Error variance appears to be homoscedastic (p = 0.334).
# 2. Normality of the residuals
# 2.1 histogram of residuals
# Normality of Residuals: Food Waste
plot(check_normality(rdt_multi_fw_p), type = "density") +
labs(title = "Normality of Residuals: Food Waste", subtitle = "")
# Normality of Residuals: Solid Food Waste
plot(check_normality(rdt_multi_sfw_p), type = "density") +
labs(title = "Normality of Residuals: Solid Food Waste", subtitle = "")
# Normality of Residuals: Liquid Food Waste
plot(check_normality(rdt_multi_lfw_p), type = "density") +
labs(title = "Normality of Residuals: Liquid Food Waste", subtitle = "")
# 2.2 Normality of Residuals
ass_multi_fw_p[[6]] +
labs(title = "QQ Plot of Residuals: Food Waste", subtitle = "")
ass_multi_sfw_p[[6]] +
labs(title = "QQ Plot of Residuals: Solid Food Waste", subtitle = "")
ass_multi_lfw_p[[6]] +
labs(title = "QQ Plot of Residuals: Liquid Food Waste", subtitle = "")
# 2.3 shapiro-wilk normality test
check_normality(rdt_multi_fw)
## Warning: Non-normality of residuals detected (p = 0.001).
check_normality(rdt_multi_sfw)
## Warning: Non-normality of residuals detected (p < .001).
check_normality(rdt_multi_lfw)
## Warning: Non-normality of residuals detected (p = 0.023).
# 3. Homoscedasticity of the residuals
# 3.1 plot residuals
ass_multi_fw_p[[3]] +
labs(title = "Homoscedasticity: Food Waste", subtitle = "")
ass_multi_sfw_p[[3]] +
labs(title = "Homoscedasticity: Solid Food Waste", subtitle = "")
ass_multi_lfw_p[[3]] +
labs(title = "Homoscedasticity: Liquid Food Waste", subtitle = "")
# 3.2 Breusch-Pagan test
lmtest::bptest(rdt_multi_fw_p)
##
## studentized Breusch-Pagan test
##
## data: rdt_multi_fw_p
## BP = 15.097, df = 9, p-value = 0.08831
lmtest::bptest(rdt_multi_sfw_p)
##
## studentized Breusch-Pagan test
##
## data: rdt_multi_sfw_p
## BP = 10.355, df = 9, p-value = 0.3225
lmtest::bptest(rdt_multi_lfw_p)
##
## studentized Breusch-Pagan test
##
## data: rdt_multi_lfw_p
## BP = 13.732, df = 9, p-value = 0.1322
# 4. No influential points (outliers)
ass_multi_fw_p[[4]] + labs(title = "Outliers: Food Waste", subtitle = "")
ass_multi_sfw_p[[4]] + labs(title = "Outliers: Solid Food Waste", subtitle = "")
ass_multi_lfw_p[[4]] + labs(title = "Outliers: Liquid Food Waste", subtitle = "")
# 5. No multicollinearity
ass_multi_fw_p[[5]] + labs(title = "VIF: Food Waste", subtitle = "")
ass_multi_sfw_p[[5]] + labs(title = "VIF: Solid Food Waste", subtitle = "")
ass_multi_lfw_p[[5]] + labs(title = "VIF: Liquid Food Waste", subtitle = "")
# 6. Independence of the observations
# Autocorrelation
check_autocorrelation(rdt_multi_fw_p)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.928).
check_autocorrelation(rdt_multi_sfw_p)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.798).
check_autocorrelation(rdt_multi_lfw_p)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.870).
polynomial model
# poly- food waste per customer -----
rdt_poly_fw_p <- food_waste_p_kg ~ container * time +
container * I(time^2) +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_poly_fw_p <- df %>%
filter(!is_closed) %>%
lm(rdt_poly_fw_p, data = .)
summary(rdt_poly_fw_p)
##
## Call:
## lm(formula = rdt_poly_fw_p, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.07797 -0.02446 -0.00292 0.02073 0.14856
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.857e-02 3.821e-02 0.486 0.6277
## container 1.680e-02 1.965e-02 0.855 0.3940
## time -3.906e-04 7.030e-04 -0.556 0.5793
## I(time^2) -2.359e-06 7.623e-06 -0.309 0.7574
## temp_c -1.356e-04 4.821e-04 -0.281 0.7789
## humi_p 1.238e-04 3.544e-04 0.349 0.7273
## prcp_mm -2.078e-03 1.562e-03 -1.330 0.1855
## liquors 1.201e-03 1.927e-03 0.623 0.5339
## sales 4.164e-05 1.925e-05 2.163 0.0321 *
## halfs 6.986e-04 1.117e-03 0.625 0.5327
## container:time 1.170e-03 1.106e-03 1.058 0.2919
## container:I(time^2) -8.306e-06 1.306e-05 -0.636 0.5257
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03805 on 149 degrees of freedom
## Multiple R-squared: 0.1274, Adjusted R-squared: 0.06295
## F-statistic: 1.977 on 11 and 149 DF, p-value: 0.0344
# poly- solid food waste per customer -----
rdt_poly_sfw_p <- solid_waste_p_kg ~ container * time +
container * I(time^2) +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_poly_sfw_p <- df %>%
filter(!is_closed) %>%
lm(rdt_poly_sfw_p, data = .)
summary(rdt_poly_sfw_p)
##
## Call:
## lm(formula = rdt_poly_sfw_p, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.027962 -0.010345 -0.002081 0.006764 0.102708
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.199e-02 1.582e-02 0.758 0.450
## container 6.501e-03 8.136e-03 0.799 0.426
## time -4.329e-04 2.910e-04 -1.487 0.139
## I(time^2) -4.166e-06 3.156e-06 -1.320 0.189
## temp_c -1.347e-04 1.996e-04 -0.675 0.501
## humi_p -5.413e-06 1.467e-04 -0.037 0.971
## prcp_mm -8.951e-04 6.468e-04 -1.384 0.168
## liquors 7.133e-04 7.977e-04 0.894 0.373
## sales 1.022e-05 7.970e-06 1.282 0.202
## halfs -3.289e-04 4.625e-04 -0.711 0.478
## container:time 6.071e-04 4.580e-04 1.326 0.187
## container:I(time^2) 1.443e-06 5.406e-06 0.267 0.790
##
## Residual standard error: 0.01575 on 149 degrees of freedom
## Multiple R-squared: 0.06457, Adjusted R-squared: -0.004489
## F-statistic: 0.935 on 11 and 149 DF, p-value: 0.5087
# poly- liquid food waste per customer -----
rdt_poly_lfw_p <- liquid_waste_p_kg ~ container * time +
container * I(time^2) +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_poly_lfw_p <- df %>%
filter(!is_closed) %>%
lm(rdt_poly_lfw_p, data = .)
summary(rdt_poly_lfw_p)
##
## Call:
## lm(formula = rdt_poly_lfw_p, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.050007 -0.018305 -0.003888 0.017097 0.076798
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.582e-03 2.697e-02 0.244 0.8076
## container 1.030e-02 1.387e-02 0.742 0.4590
## time 4.226e-05 4.962e-04 0.085 0.9322
## I(time^2) 1.808e-06 5.381e-06 0.336 0.7374
## temp_c -8.461e-07 3.403e-04 -0.002 0.9980
## humi_p 1.292e-04 2.502e-04 0.517 0.6062
## prcp_mm -1.183e-03 1.103e-03 -1.073 0.2851
## liquors 4.880e-04 1.360e-03 0.359 0.7203
## sales 3.143e-05 1.359e-05 2.313 0.0221 *
## halfs 1.028e-03 7.887e-04 1.303 0.1947
## container:time 5.629e-04 7.809e-04 0.721 0.4721
## container:I(time^2) -9.749e-06 9.218e-06 -1.058 0.2920
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02686 on 149 degrees of freedom
## Multiple R-squared: 0.1761, Adjusted R-squared: 0.1152
## F-statistic: 2.895 on 11 and 149 DF, p-value: 0.00176
Ass-Poly
- Linearity of the relationships between the dependent and independent
variables
- Normality of the residuals
- Homoscedasticity of the residuals
- No influential points (outliers)
- No multicollinearity
- Independence of the observations
library(performance)
ass_poly_fw_p <- plot(check_model(rdt_poly_fw_p, detrend=FALSE,panel = FALSE))
ass_poly_sfw_p <- plot(check_model(rdt_poly_sfw_p, detrend=FALSE,panel = FALSE))
ass_poly_lfw_p <- plot(check_model(rdt_poly_lfw_p, detrend=FALSE,panel = FALSE))
# 1. Linearity of the relationships between the dependent and independent variables
# 1.1 plot residual vs fitted values
ass_poly_fw_p[[2]] + labs(title = "Linearity: Food Waste", subtitle = "")
ass_poly_sfw_p[[2]] + labs(title = "Linearity: Solid Food Waste", subtitle = "")
ass_poly_lfw_p[[2]] + labs(title = "Linearity: Liquid Food Waste", subtitle = "")
# 1.2 check linearity
check_heteroscedasticity(rdt_poly_fw_p)
## OK: Error variance appears to be homoscedastic (p = 0.107).
check_heteroscedasticity(rdt_poly_sfw_p)
## Warning: Heteroscedasticity (non-constant error variance) detected (p = 0.004).
check_heteroscedasticity(rdt_poly_lfw_p)
## OK: Error variance appears to be homoscedastic (p = 0.321).
# 2. Normality of the residuals
# 2.1 histogram of residuals
# Normality of Residuals: Food Waste
plot(check_normality(rdt_poly_fw_p), type = "density") +
labs(title = "Normality of Residuals: Food Waste", subtitle = "")
# Normality of Residuals: Solid Food Waste
plot(check_normality(rdt_poly_sfw_p), type = "density") +
labs(title = "Normality of Residuals: Solid Food Waste", subtitle = "")
# Normality of Residuals: Liquid Food Waste
plot(check_normality(rdt_poly_lfw_p), type = "density") +
labs(title = "Normality of Residuals: Liquid Food Waste", subtitle = "")
# 2.2 Normality of Residuals
ass_poly_fw_p[[6]] +
labs(title = "QQ Plot of Residuals: Food Waste", subtitle = "")
ass_poly_sfw_p[[6]] +
labs(title = "QQ Plot of Residuals: Solid Food Waste", subtitle = "")
ass_poly_lfw_p[[6]] +
labs(title = "QQ Plot of Residuals: Liquid Food Waste", subtitle = "")
# 2.3 shapiro-wilk normality test
check_normality(rdt_poly_fw_p)
## Warning: Non-normality of residuals detected (p = 0.012).
check_normality(rdt_poly_sfw_p)
## Warning: Non-normality of residuals detected (p < .001).
check_normality(rdt_poly_lfw_p)
## Warning: Non-normality of residuals detected (p = 0.023).
# 3. Homoscedasticity of the residuals
# 3.1 plot residuals
ass_poly_fw_p[[3]] +
labs(title = "Homoscedasticity: Food Waste", subtitle = "")
ass_poly_sfw_p[[3]] +
labs(title = "Homoscedasticity: Solid Food Waste", subtitle = "")
ass_poly_lfw_p[[3]] +
labs(title = "Homoscedasticity: Liquid Food Waste", subtitle = "")
# 3.2 Breusch-Pagan test
lmtest::bptest(rdt_poly_fw_p)
##
## studentized Breusch-Pagan test
##
## data: rdt_poly_fw_p
## BP = 17.523, df = 11, p-value = 0.09332
lmtest::bptest(rdt_poly_sfw_p)
##
## studentized Breusch-Pagan test
##
## data: rdt_poly_sfw_p
## BP = 11.556, df = 11, p-value = 0.3979
lmtest::bptest(rdt_poly_lfw_p)
##
## studentized Breusch-Pagan test
##
## data: rdt_poly_lfw_p
## BP = 16.922, df = 11, p-value = 0.1102
# 4. No influential points (outliers)
ass_poly_fw_p[[4]] + labs(title = "Outliers: Food Waste", subtitle = "")
ass_poly_sfw_p[[4]] + labs(title = "Outliers: Solid Food Waste", subtitle = "")
ass_poly_lfw_p[[4]] + labs(title = "Outliers: Liquid Food Waste", subtitle = "")
# 5. No multicollinearity
ass_poly_fw_p[[5]] + labs(title = "VIF: Food Waste", subtitle = "")
ass_poly_sfw_p[[5]] + labs(title = "VIF: Solid Food Waste", subtitle = "")
ass_poly_lfw_p[[5]] + labs(title = "VIF: Liquid Food Waste", subtitle = "")
# 6. Independence of the observations
# Autocorrelation
check_autocorrelation(rdt_poly_fw_p)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.922).
check_autocorrelation(rdt_poly_sfw_p)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.778).
check_autocorrelation(rdt_poly_lfw_p)
## OK: Residuals appear to be independent and not autocorrelated (p = 0.874).
Cubic model
# poly- food waste per customer -----
rdt_poly3_fw_p <- food_waste_p_kg ~ container * time +
container * I(time^2) + container * I(time^3) +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_poly3_fw_p <- df %>%
filter(!is_closed) %>%
lm(rdt_poly3_fw_p, data = .)
summary(rdt_poly3_fw_p)
##
## Call:
## lm(formula = rdt_poly3_fw_p, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.075327 -0.024795 -0.003087 0.023996 0.150164
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.343e-03 3.869e-02 0.164 0.8700
## container 3.233e-02 2.412e-02 1.340 0.1823
## time -3.297e-03 1.750e-03 -1.885 0.0615 .
## I(time^2) -8.808e-05 4.813e-05 -1.830 0.0693 .
## I(time^3) -6.576e-07 3.644e-07 -1.805 0.0732 .
## temp_c 8.336e-05 5.189e-04 0.161 0.8726
## humi_p 6.240e-05 3.559e-04 0.175 0.8610
## prcp_mm -2.544e-03 1.577e-03 -1.613 0.1089
## liquors 8.443e-04 1.928e-03 0.438 0.6621
## sales 4.040e-05 1.920e-05 2.105 0.0370 *
## halfs 9.267e-04 1.120e-03 0.827 0.4093
## container:time 4.590e-03 2.677e-03 1.714 0.0886 .
## container:I(time^2) 6.111e-05 8.238e-05 0.742 0.4593
## container:I(time^3) 7.933e-07 6.624e-07 1.198 0.2330
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03787 on 147 degrees of freedom
## Multiple R-squared: 0.147, Adjusted R-squared: 0.07153
## F-statistic: 1.948 on 13 and 147 DF, p-value: 0.02915
# poly- solid food waste per customer -----
rdt_poly3_sfw_p <- solid_waste_p_kg ~ container * time +
container * I(time^2) + container * I(time^3) +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_poly3_sfw_p <- df %>%
filter(!is_closed) %>%
lm(rdt_poly3_sfw_p, data = .)
summary(rdt_poly3_sfw_p)
##
## Call:
## lm(formula = rdt_poly3_sfw_p, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.028116 -0.010438 -0.001034 0.006871 0.103441
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6.987e-03 1.601e-02 0.436 0.6632
## container 1.338e-02 9.986e-03 1.340 0.1822
## time -1.653e-03 7.242e-04 -2.282 0.0239 *
## I(time^2) -4.020e-05 1.992e-05 -2.018 0.0454 *
## I(time^3) -2.764e-07 1.508e-07 -1.832 0.0690 .
## temp_c -3.778e-05 2.148e-04 -0.176 0.8606
## humi_p -3.221e-05 1.473e-04 -0.219 0.8272
## prcp_mm -1.087e-03 6.528e-04 -1.664 0.0981 .
## liquors 5.660e-04 7.981e-04 0.709 0.4793
## sales 9.731e-06 7.946e-06 1.225 0.2226
## halfs -2.356e-04 4.636e-04 -0.508 0.6121
## container:time 1.973e-03 1.108e-03 1.780 0.0771 .
## container:I(time^2) 3.300e-05 3.410e-05 0.968 0.3347
## container:I(time^3) 3.118e-07 2.742e-07 1.137 0.2573
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01568 on 147 degrees of freedom
## Multiple R-squared: 0.08587, Adjusted R-squared: 0.005027
## F-statistic: 1.062 on 13 and 147 DF, p-value: 0.3965
# poly- liquid food waste per customer -----
rdt_poly3_lfw_p <- liquid_waste_p_kg ~ container * time +
container * I(time^2) + container * I(time^3) +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_poly3_lfw_p <- df %>%
filter(!is_closed) %>%
lm(rdt_poly3_lfw_p, data = .)
summary(rdt_poly3_lfw_p)
##
## Call:
## lm(formula = rdt_poly3_lfw_p, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.050013 -0.019310 -0.003956 0.016136 0.074786
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.442e-04 2.741e-02 -0.024 0.9813
## container 1.895e-02 1.709e-02 1.108 0.2695
## time -1.644e-03 1.240e-03 -1.327 0.1867
## I(time^2) -4.788e-05 3.410e-05 -1.404 0.1623
## I(time^3) -3.813e-07 2.582e-07 -1.477 0.1419
## temp_c 1.211e-04 3.676e-04 0.330 0.7422
## humi_p 9.461e-05 2.521e-04 0.375 0.7080
## prcp_mm -1.457e-03 1.117e-03 -1.304 0.1942
## liquors 2.783e-04 1.366e-03 0.204 0.8389
## sales 3.067e-05 1.360e-05 2.255 0.0256 *
## halfs 1.162e-03 7.935e-04 1.465 0.1451
## container:time 2.617e-03 1.897e-03 1.380 0.1698
## container:I(time^2) 2.811e-05 5.836e-05 0.482 0.6308
## container:I(time^3) 4.815e-07 4.693e-07 1.026 0.3066
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02683 on 147 degrees of freedom
## Multiple R-squared: 0.1887, Adjusted R-squared: 0.117
## F-statistic: 2.631 on 13 and 147 DF, p-value: 0.002514
Quartic model
# poly- food waste per customer -----
rdt_poly4_fw_p <- food_waste_p_kg ~ container * time +
container * I(time^2) + container * I(time^3) + container * I(time^4) +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_poly4_fw_p <- df %>%
filter(!is_closed) %>%
lm(rdt_poly4_fw_p, data = .)
summary(rdt_poly4_fw_p)
##
## Call:
## lm(formula = rdt_poly4_fw_p, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.075470 -0.024339 -0.003301 0.023904 0.148010
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.489e-02 4.337e-02 0.343 0.7318
## container 2.588e-02 3.012e-02 0.859 0.3916
## time -1.926e-03 3.541e-03 -0.544 0.5873
## I(time^2) -1.803e-05 1.643e-04 -0.110 0.9128
## I(time^3) 5.983e-07 2.839e-06 0.211 0.8334
## I(time^4) 7.256e-09 1.627e-08 0.446 0.6562
## temp_c 9.970e-05 5.242e-04 0.190 0.8494
## humi_p 3.479e-05 3.634e-04 0.096 0.9239
## prcp_mm -2.586e-03 1.593e-03 -1.623 0.1068
## liquors 8.721e-04 1.946e-03 0.448 0.6547
## sales 4.080e-05 1.934e-05 2.109 0.0366 *
## halfs 9.161e-04 1.127e-03 0.813 0.4178
## container:time 3.297e-03 5.307e-03 0.621 0.5354
## container:I(time^2) -1.457e-05 2.720e-04 -0.054 0.9573
## container:I(time^3) -3.401e-07 5.263e-06 -0.065 0.9486
## container:I(time^4) -8.117e-09 3.353e-08 -0.242 0.8091
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03811 on 145 degrees of freedom
## Multiple R-squared: 0.1481, Adjusted R-squared: 0.06002
## F-statistic: 1.681 on 15 and 145 DF, p-value: 0.0607
# poly- solid food waste per customer -----
rdt_poly4_sfw_p <- solid_waste_p_kg ~ container * time +
container * I(time^2) + container * I(time^3) + container * I(time^4) +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_poly4_sfw_p <- df %>%
filter(!is_closed) %>%
lm(rdt_poly4_sfw_p, data = .)
summary(rdt_poly4_sfw_p)
##
## Call:
## lm(formula = rdt_poly4_sfw_p, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.026376 -0.010198 -0.001428 0.006948 0.100389
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.889e-02 1.779e-02 1.062 0.2899
## container 1.075e-03 1.235e-02 0.087 0.9307
## time 2.333e-04 1.452e-03 0.161 0.8726
## I(time^2) 5.598e-05 6.736e-05 0.831 0.4073
## I(time^3) 1.445e-06 1.164e-06 1.241 0.2166
## I(time^4) 9.930e-09 6.670e-09 1.489 0.1387
## temp_c -2.364e-05 2.150e-04 -0.110 0.9126
## humi_p -7.340e-05 1.490e-04 -0.493 0.6231
## prcp_mm -1.114e-03 6.533e-04 -1.705 0.0904 .
## liquors 5.647e-04 7.979e-04 0.708 0.4803
## sales 1.042e-05 7.931e-06 1.313 0.1911
## halfs -2.575e-04 4.623e-04 -0.557 0.5785
## container:time 1.206e-03 2.176e-03 0.554 0.5803
## container:I(time^2) -1.334e-04 1.115e-04 -1.196 0.2335
## container:I(time^3) 7.772e-08 2.158e-06 0.036 0.9713
## container:I(time^4) -2.000e-08 1.375e-08 -1.455 0.1479
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01563 on 145 degrees of freedom
## Multiple R-squared: 0.104, Adjusted R-squared: 0.01131
## F-statistic: 1.122 on 15 and 145 DF, p-value: 0.3418
# poly- liquid food waste per customer -----
rdt_poly4_lfw_p <- liquid_waste_p_kg ~ container * time +
container * I(time^2) + container * I(time^3) + container * I(time^4) +
temp_c + humi_p + prcp_mm +
liquors + sales + halfs
rdt_poly4_lfw_p <- df %>%
filter(!is_closed) %>%
lm(rdt_poly4_lfw_p, data = .)
summary(rdt_poly4_lfw_p)
##
## Call:
## lm(formula = rdt_poly4_lfw_p, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.04932 -0.01875 -0.00414 0.01605 0.07566
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.000e-03 3.072e-02 -0.130 0.8966
## container 2.480e-02 2.133e-02 1.163 0.2469
## time -2.159e-03 2.508e-03 -0.861 0.3907
## I(time^2) -7.401e-05 1.164e-04 -0.636 0.5257
## I(time^3) -8.463e-07 2.011e-06 -0.421 0.6745
## I(time^4) -2.674e-09 1.152e-08 -0.232 0.8168
## temp_c 1.233e-04 3.713e-04 0.332 0.7403
## humi_p 1.082e-04 2.574e-04 0.420 0.6749
## prcp_mm -1.472e-03 1.129e-03 -1.304 0.1942
## liquors 3.074e-04 1.378e-03 0.223 0.8238
## sales 3.038e-05 1.370e-05 2.218 0.0281 *
## halfs 1.174e-03 7.986e-04 1.470 0.1438
## container:time 2.091e-03 3.760e-03 0.556 0.5789
## container:I(time^2) 1.189e-04 1.927e-04 0.617 0.5383
## container:I(time^3) -4.178e-07 3.728e-06 -0.112 0.9109
## container:I(time^4) 1.189e-08 2.375e-08 0.500 0.6175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02699 on 145 degrees of freedom
## Multiple R-squared: 0.1901, Adjusted R-squared: 0.1064
## F-statistic: 2.269 on 15 and 145 DF, p-value: 0.006618
Some model
# sales base model
rdt_sales <- sales ~ container * time
rdt_sales <- df %>%
filter(!is_closed) %>%
lm(rdt_sales, data = .)
summary(rdt_sales)
##
## Call:
## lm(formula = rdt_sales, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -464.65 -156.75 -7.94 132.75 452.58
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 653.2167 45.3342 14.409 <2e-16 ***
## container -21.1864 65.7665 -0.322 0.7478
## time -2.1632 0.9051 -2.390 0.0180 *
## container:time 3.0966 1.4334 2.160 0.0323 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 208.4 on 157 degrees of freedom
## Multiple R-squared: 0.07338, Adjusted R-squared: 0.05568
## F-statistic: 4.145 on 3 and 157 DF, p-value: 0.00737
# multiple -----
rdt_sales_mult <- sales ~ container * time +
temp_c + humi_p + prcp_mm +
liquors + halfs
rdt_sales_mult <- df %>%
filter(!is_closed) %>%
lm(rdt_sales_mult, data = .)
summary(rdt_sales_mult)
##
## Call:
## lm(formula = rdt_sales_mult, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -400.38 -104.13 -9.09 93.36 439.38
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 382.140 147.670 2.588 0.0106 *
## container -31.487 58.605 -0.537 0.5919
## time -1.011 1.049 -0.964 0.3367
## temp_c 2.704 2.011 1.345 0.1807
## humi_p 1.429 1.481 0.965 0.3362
## prcp_mm -5.455 6.451 -0.846 0.3991
## liquors 43.158 7.347 5.874 2.59e-08 ***
## halfs 28.934 4.069 7.110 4.23e-11 ***
## container:time 1.546 1.601 0.966 0.3357
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 160.9 on 152 degrees of freedom
## Multiple R-squared: 0.4648, Adjusted R-squared: 0.4366
## F-statistic: 16.5 on 8 and 152 DF, p-value: < 2.2e-16
# poly- liquid food waste per customer -----
rdt_sales <- sales ~ container * time + container * I(time^2) +
temp_c + humi_p + prcp_mm +
liquors + halfs
rdt_sales <- df %>%
filter(!is_closed) %>%
lm(rdt_sales, data = .)
summary(rdt_sales)
##
## Call:
## lm(formula = rdt_sales, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -398.79 -101.98 -7.22 94.60 437.60
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 441.27206 157.99058 2.793 0.0059 **
## container -63.00940 83.19361 -0.757 0.4500
## time 1.96129 2.97708 0.659 0.5110
## I(time^2) 0.03465 0.03220 1.076 0.2837
## temp_c 2.57848 2.03385 1.268 0.2068
## humi_p 1.20252 1.49982 0.802 0.4239
## prcp_mm -4.47457 6.61600 -0.676 0.4999
## liquors 42.38676 7.40288 5.726 5.44e-08 ***
## halfs 29.22421 4.09386 7.139 3.77e-11 ***
## container:time -2.27212 4.68807 -0.485 0.6286
## container:I(time^2) -0.02417 0.05535 -0.437 0.6630
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 161.4 on 150 degrees of freedom
## Multiple R-squared: 0.4691, Adjusted R-squared: 0.4337
## F-statistic: 13.25 on 10 and 150 DF, p-value: < 2.2e-16
per Customer
library(purrr)
library(tidyr)
library(tidyverse)
## Warning: package 'stringr' was built under R version 4.2.3
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ lubridate 1.9.3 ✔ tibble 3.2.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(broom)
bandwidth = 25
# Local Regression of container on food_waste_kg ----
loc_fw_p <- df %>%
filter(!is_closed) %>%
filter(abs(time) <= bandwidth) %>%
lm(food_waste_p_kg ~ container, data = .)
summary(loc_fw_p)
##
## Call:
## lm(formula = food_waste_p_kg ~ container, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.071951 -0.021391 0.001999 0.021733 0.079921
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.066985 0.007257 9.230 2.69e-12 ***
## container 0.016731 0.010164 1.646 0.106
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03629 on 49 degrees of freedom
## Multiple R-squared: 0.0524, Adjusted R-squared: 0.03306
## F-statistic: 2.709 on 1 and 49 DF, p-value: 0.1062
# Local Regression of container on solid food_waste_kg ----
loc_sfw_p <- df %>%
filter(!is_closed) %>%
filter(abs(time) <= bandwidth) %>%
lm(solid_waste_p_kg ~ container, data = .)
summary(loc_sfw_p)
##
## Call:
## lm(formula = solid_waste_p_kg ~ container, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.022725 -0.006960 -0.002115 0.007308 0.025352
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.022725 0.002242 10.135 1.29e-13 ***
## container 0.001804 0.003140 0.575 0.568
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01121 on 49 degrees of freedom
## Multiple R-squared: 0.006691, Adjusted R-squared: -0.01358
## F-statistic: 0.3301 on 1 and 49 DF, p-value: 0.5682
# Local Regression of container on liquid food_waste_kg----
loc_lfw_p <- df %>%
filter(!is_closed) %>%
filter(abs(time) <= bandwidth) %>%
lm(liquid_waste_p_kg ~ container, data = .)
summary(loc_lfw_p)
##
## Call:
## lm(formula = liquid_waste_p_kg ~ container, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.056246 -0.019655 0.000813 0.019476 0.068086
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.044261 0.005547 7.979 2.06e-10 ***
## container 0.014926 0.007769 1.921 0.0605 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02774 on 49 degrees of freedom
## Multiple R-squared: 0.07005, Adjusted R-squared: 0.05107
## F-statistic: 3.691 on 1 and 49 DF, p-value: 0.06053
Interaction
bandwidth = 25
# Local Regression of container on food_waste_kg ----
loc_fw_p <- df %>%
filter(!is_closed) %>%
filter(abs(time) <= bandwidth) %>%
lm(food_waste_p_kg ~ container*time, data = .)
summary(loc_fw_p)
##
## Call:
## lm(formula = food_waste_p_kg ~ container * time, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.076915 -0.019486 -0.000832 0.025310 0.076839
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0552496 0.0151211 3.654 0.000649 ***
## container 0.0233299 0.0205930 1.133 0.263003
## time -0.0009027 0.0010172 -0.888 0.379327
## container:time 0.0013136 0.0013979 0.940 0.352184
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.03667 on 47 degrees of freedom
## Multiple R-squared: 0.07158, Adjusted R-squared: 0.01232
## F-statistic: 1.208 on 3 and 47 DF, p-value: 0.3172
# Local Regression of container on solid food_waste_kg ----
loc_sfw_p <- df %>%
filter(!is_closed) %>%
filter(abs(time) <= bandwidth) %>%
lm(solid_waste_p_kg ~ container*time, data = .)
summary(loc_sfw_p)
##
## Call:
## lm(formula = solid_waste_p_kg ~ container * time, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.025961 -0.006713 -0.002136 0.008028 0.024176
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0189002 0.0046765 4.042 0.000195 ***
## container 0.0054568 0.0063688 0.857 0.395900
## time -0.0002942 0.0003146 -0.935 0.354488
## container:time 0.0003079 0.0004323 0.712 0.479863
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01134 on 47 degrees of freedom
## Multiple R-squared: 0.02488, Adjusted R-squared: -0.03736
## F-statistic: 0.3997 on 3 and 47 DF, p-value: 0.7538
# Local Regression of container on liquid food_waste_kg----
loc_lfw_p <- df %>%
filter(!is_closed) %>%
filter(abs(time) <= bandwidth) %>%
lm(liquid_waste_p_kg ~ container*time, data = .)
summary(loc_lfw_p)
##
## Call:
## lm(formula = liquid_waste_p_kg ~ container * time, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.059985 -0.016844 0.000476 0.019769 0.065107
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.0363494 0.0115661 3.143 0.0029 **
## container 0.0178730 0.0157515 1.135 0.2623
## time -0.0006086 0.0007780 -0.782 0.4380
## container:time 0.0010057 0.0010693 0.941 0.3517
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.02805 on 47 degrees of freedom
## Multiple R-squared: 0.08762, Adjusted R-squared: 0.02938
## F-statistic: 1.504 on 3 and 47 DF, p-value: 0.2256
# Visualization local regression ----
locals <- tibble(bandwidth = seq(from = 6, to = 30, by = 1))
results_local <- locals %>%
group_by(bandwidth) %>%
mutate(
loc_reg_p = map(bandwidth, ~ lm(food_waste_p_kg ~ container * time,
data = subset(df, is_closed == FALSE),
subset = (abs(time) <= bandwidth))),
tidied = map(loc_reg_p,tidy,conf.int = TRUE)
) %>%
unnest(tidied) %>%
filter(term == "container")
results_local %>%
ggplot(aes(x = bandwidth, y = estimate,
ymin = conf.low, ymax = conf.high)) +
geom_point() +
geom_pointrange() +
geom_hline(yintercept = 0, linetype = "dashed") +
xlab("Band Width") + ylab("Estimated Effect") +
ggtitle("Estimated Container Charge Effect on Food Waste with Interaction")
Multiple
bandwidth = 25
# Local Regression of container on food_waste_kg ----
loc_fw_p <- df %>%
filter(!is_closed) %>%
filter(abs(time) <= bandwidth) %>%
lm(food_waste_p_kg ~ container*time
+ temp_c + humi_p + prcp_mm + liquors + sales + halfs,
data = .)
summary(loc_fw_p)
##
## Call:
## lm(formula = food_waste_p_kg ~ container * time + temp_c + humi_p +
## prcp_mm + liquors + sales + halfs, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.070198 -0.018478 -0.000523 0.021209 0.070930
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 7.963e-02 7.727e-02 1.031 0.309
## container 1.269e-02 2.150e-02 0.590 0.558
## time -7.400e-04 1.049e-03 -0.706 0.484
## temp_c 1.008e-03 8.579e-04 1.174 0.247
## humi_p -4.189e-04 8.007e-04 -0.523 0.604
## prcp_mm -3.606e-03 3.060e-03 -1.178 0.245
## liquors 8.516e-04 3.177e-03 0.268 0.790
## sales 3.606e-05 3.459e-05 1.043 0.303
## halfs 3.668e-04 2.245e-03 0.163 0.871
## container:time 1.555e-03 1.585e-03 0.981 0.332
##
## Residual standard error: 0.03567 on 41 degrees of freedom
## Multiple R-squared: 0.2336, Adjusted R-squared: 0.06541
## F-statistic: 1.389 on 9 and 41 DF, p-value: 0.2247
# Local Regression of container on solid food_waste_kg ----
loc_sfw_p <- df %>%
filter(!is_closed) %>%
filter(abs(time) <= bandwidth) %>%
lm(solid_waste_p_kg ~ container*time
+ temp_c + humi_p + prcp_mm + liquors + sales + halfs,
data = .)
summary(loc_sfw_p)
##
## Call:
## lm(formula = solid_waste_p_kg ~ container * time + temp_c + humi_p +
## prcp_mm + liquors + sales + halfs, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.0186022 -0.0056049 -0.0005597 0.0058769 0.0210676
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3.528e-02 2.293e-02 1.539 0.1315
## container 1.389e-03 6.381e-03 0.218 0.8288
## time -2.229e-04 3.112e-04 -0.716 0.4778
## temp_c 5.332e-04 2.546e-04 2.095 0.0424 *
## humi_p -2.283e-04 2.376e-04 -0.961 0.3421
## prcp_mm -8.376e-04 9.079e-04 -0.923 0.3616
## liquors 1.705e-04 9.426e-04 0.181 0.8573
## sales 1.810e-05 1.026e-05 1.763 0.0853 .
## halfs -4.264e-04 6.661e-04 -0.640 0.5257
## container:time 3.030e-04 4.703e-04 0.644 0.5230
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01058 on 41 degrees of freedom
## Multiple R-squared: 0.2592, Adjusted R-squared: 0.09653
## F-statistic: 1.594 on 9 and 41 DF, p-value: 0.1495
# Local Regression of container on liquid food_waste_kg----
loc_lfw_p <- df %>%
filter(!is_closed) %>%
filter(abs(time) <= bandwidth) %>%
lm(liquid_waste_p_kg ~ container*time
+ temp_c + humi_p + prcp_mm + liquors + sales + halfs,
data = .)
summary(loc_lfw_p)
##
## Call:
## lm(formula = liquid_waste_p_kg ~ container * time + temp_c +
## humi_p + prcp_mm + liquors + sales + halfs, data = .)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.051596 -0.015189 -0.001578 0.016023 0.063417
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.436e-02 6.054e-02 0.733 0.468
## container 1.130e-02 1.685e-02 0.671 0.506
## time -5.171e-04 8.218e-04 -0.629 0.533
## temp_c 4.743e-04 6.722e-04 0.706 0.484
## humi_p -1.905e-04 6.274e-04 -0.304 0.763
## prcp_mm -2.768e-03 2.398e-03 -1.155 0.255
## liquors 6.810e-04 2.489e-03 0.274 0.786
## sales 1.797e-05 2.710e-05 0.663 0.511
## halfs 7.931e-04 1.759e-03 0.451 0.654
## container:time 1.252e-03 1.242e-03 1.008 0.319
##
## Residual standard error: 0.02795 on 41 degrees of freedom
## Multiple R-squared: 0.2097, Adjusted R-squared: 0.03621
## F-statistic: 1.209 on 9 and 41 DF, p-value: 0.3161
# Visualization ----
# Local regression with multiple
results_local_multi <- locals %>%
group_by(bandwidth) %>%
mutate(
loc_reg_p = map(bandwidth, ~ lm(food_waste_p_kg ~ container * time
+ temp_c + humi_p + prcp_mm
+ liquors + sales + halfs,
data = subset(df, is_closed == FALSE),
subset = (abs(time) <= bandwidth))),
tidied = map(loc_reg_p,tidy,conf.int = TRUE)
) %>%
unnest(tidied) %>%
filter(term == "container")
results_local_multi %>%
ggplot(aes(x = bandwidth, y = estimate,
ymin = conf.low, ymax = conf.high)) +
geom_point() +
geom_pointrange() +
geom_hline(yintercept = 0, linetype = "dashed") +
xlab("Band Width") + ylab("Estimated Effect") +
ggtitle("Estimated Container Charge Effect on Food Waste with Multiple Model")
# Donut regression with multiple
results_local_multi_donut <- locals %>%
group_by(bandwidth) %>%
mutate(
loc_reg_p = map(bandwidth, ~ lm(food_waste_p_kg ~ container * time
+ temp_c + humi_p + prcp_mm
+ liquors + sales + halfs,
data = subset(df, is_closed == FALSE),
subset = (abs(time) > bandwidth))),
tidied = map(loc_reg_p,tidy,conf.int = TRUE)
) %>%
unnest(tidied) %>%
filter(term == "container")
results_local_multi_donut %>%
ggplot(aes(x = bandwidth, y = estimate,
ymin = conf.low, ymax = conf.high)) +
geom_point() +
geom_pointrange() +
geom_hline(yintercept = 0, linetype = "dashed") +
xlab("Band Width") + ylab("Estimated Effect") +
ggtitle("Estimated Container Charge Effect on Food Waste with Multiple Model")
